Partially Identifying the Prevalence of Health Insurance Given Contaminated Sampling Response Error

This paper derives simple closed-form identification regions for the U.S. nonelderly population's prevalence of health insurance coverage in the presence of household reporting errors. The methods extend Horowitz and Manski's (1995) nonparametric analysis of contaminated samples for the case that the outcome is binary. In this case, draws from the alternative distribution (i.e., not the distribution of interest) might naturally be defined as response errors. The derived identification regions can dramatically reduce the degree of uncertainty about the outcome distribution compared with the contaminated sampling bounds. These regions are estimated using data from the Medical Expenditure Panel Survey (MEPS) combined with health insurance validation data available for a nonrandom portion of the sample.partial identification; nonparametric bounds; contaminated sampling; classification error

Aluminum oxide barriers in MCrAlY superalloy systems

An investigation was made of sputtered aluminum oxide diffusion barriers to protect gas turbine engine blade and vane alloys from their coatings. MAR M200 + Hf coated with sputtered NiCoCrAlY and MAR M509 coated with sputtered FeCrAlY were obtained both with and without 1 and 2 micron sputtered Al2O3 barrier layers. Electron dispersive X-ray analysis was used to determine the concentration profiles of as-received and heat treated samples

Food Stamps and Food Insecurity: What Can Be Learned in the Presence of Non-Classical Measurement Error?

Policymakers have been puzzled to observe that food stamp households appear more likely to be food insecure than observationally similar eligible nonparticipating households. We reexamine this issue allowing for nonclassical reporting errors in food stamp participation and food insecurity. Extending the literature on partially identified parameters, we introduce a nonparametric framework that makes transparent what can be known about conditional probabilities when a binary outcome and conditioning variable are both subject to nonclassical measurement error. We find that the food insecurity paradox hinges on strong assumptions about the reliability of the data that are not supported by the previous food stamp participation literature.

Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean Independence

We consider the problem of identifying a mean outcome in corrupt sampling where the observed outcome is a mixture of the distribution of interest and some other distribution. We make two contributions to this literature. First, the statistical independence assumption maintained under contaminated sampling is relaxed to the weaker assumption that the outcome is mean independent of the mixing process. We then generalize this restriction to allow the two conditional means to differ by a known or bounded factor of proportionality. Second, in the special case of a binary outcome, we consider the possibility that draws from the alternative distribution are known to be erroneous, as might be the case in a mixture model of response error. We illustrate how these assumptions can be used to inform researchers about the population's use of illicit drugs in the presence of nonrandom reporting errors. In this application, we find that a response error model with multiplicative mean independence is easy to motivate and can have substantial identifying power.

Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors

Measurement error in health and disability status has been widely accepted as a central problem for social science research. Long-standing debates about the prevalence of disability, the role of health in labor market outcomes, and the influence of federal disability policy on declining employment rates have all emphasized issues regarding the reliability of self-reported disability. In addition to random error, inaccuracy in survey datasets may be produced by a host of economic, social, and psychological factors that can lead respondents to misreport work capacity. We develop a nonparametric foundation for assessing how assumptions on the reporting error process affect inferences on the employment gap between the disabled and nondisabled. Rather than imposing the strong assumptions required to obtain point identification, we derive sets of bounds that formalize the identifying power of primitive nonparametric assumptions that appear to share broad consensus in the literature. Within this framework, we introduce a finite-sample correction for the analog estimator of the monotone instrumental variable (MIV) bound. Our empirical results suggest that conclusions derived from conventional latent variable reporting error models may be driven largely by ad hoc distributional and functional form restrictions. Under relatively weak nonparametric assumptions, nonworkers appear to systematically overreport disability.

Inferring Disability Status from Corrupt Data

In light of widespread concerns about the reliability of self-reported disability, we investigate what can be learned about the prevalence of work disability under various assumptions on the reporting error process. Developing a nonparametric bounding framework, we provide tight inferences under our strongest assumptions but then find that identification deteriorates rapidly as the assumptions are relaxed. For example, we find that inferences are highly sensitive to how one models potential inconsistencies between subjective self-assessments of work limitation and more objective measures of functional limitation. These two indicators appear to measure markedly different aspects of health status.

Partially Identifying Treatment Effects with an Application to Covering the Uninsured

We extend the nonparametric literature on partially identified probability distributions and use our analytical results to provide sharp bounds on the impact of universal health insurance on provider visits and medical expenditures. Our approach accounts for uncertainty about the reliability of self-reported insurance status as well as uncertainty created by unknown counterfactuals. We construct health insurance validation data using detailed information from the Medical Expenditure Panel Survey. Imposing relatively weak nonparametric assumptions, we estimate that under universal coverage monthly per capita provider visits and expenditures would rise by less than 8% and 16%, respectively, across the nonelderly population.

Modal element method for scattering of sound by absorbing bodies

The modal element method for acoustic scattering from 2-D body is presented. The body may be acoustically soft (absorbing) or hard (reflecting). The infinite computational region is divided into two subdomains - the bounded finite element domain, which is characterized by complicated geometry and/or variable material properties, and the surrounding unbounded homogeneous domain. The acoustic pressure field is represented approximately in the finite element domain by a finite element solution, and is represented analytically by an eigenfunction expansion in the homogeneous domain. The two solutions are coupled by the continuity of pressure and velocity across the interface between the two subdomains. Also, for hard bodies, a compact modal ring grid system is introduced for which computing requirements are drastically reduced. Analysis for 2-D scattering from solid and coated (acoustically treated) bodies is presented, and several simple numerical examples are discussed. In addition, criteria are presented for determining the number of modes to accurately resolve the scattered pressure field from a solid cylinder as a function of the frequency of the incoming wave and the radius of the cylinder